42 citations · 266 across the 59 of their papers we have counts for
63 papers
Recurrent network dynamics explain the time course of perceptual grouping in natural scenes
Sami Mollard, Alekh K. Ashok, Lore Goetschalckx +4
How the brain groups image elements into coherent objects in natural scenes remains unclear. Existing models for grouping in biological vision rely on simplified stimuli with expli…
From Interpretability Methods to Interpretable Models
Julien Colin, Nuria Oliver, Thomas Serre
More than a decade in, explainable AI (XAI) for computer vision has assembled a mature toolbox: attribution, feature visualization, concept-based, and circuit-based methods. Yet al…
Structuring Sparsity: Block-Sparse Featurizers Capture Visual Concept Manifolds
Thomas Fel, Matthew Kowal, Mozes Jacobs +22
What is the geometry of a visual percept? The most widely used protocols for decomposing neural network representations into interpretable parts treat concepts as isolated directio…
A Unifying Framework for Concept-Based Representational Similarity
Grégoire Dhimoïla, Victor Boutin, Agustin Martin Picard +2
Learned representations across models and modalities often exhibit striking structural similarities, suggesting shared underlying concept decompositions. However, concept alignment…
Not Too Generative, Not Too Discriminative: The Human Alignment Sweet Spot
Jorge Chang Ortega, Bastien Le Lan, Thomas Serre +1
A central question in computational vision is whether human-like visual representations are better explained by discriminative or generative learning. Existing comparisons, however…
Capability Interpretability: Human Interpretability of Vision Foundation Models
Julien Colin, Lore Goetschalckx, Nuria Oliver +1
How interpretable are the features of leading vision models? The question is increasingly pressing as these models move from research benchmarks into high-stakes deployments, yet e…